MARC 닫기

• LDR

  05453cam a2200541M 4500

• 001

  000001334812

• 005

  20210114163529

• 006

  m d

• 007

  cr |||||||||||

• 008

  200524s2020 xx o ||| 0 eng d

• 019

  ▼a  1155328035

• 020

  ▼a  1789953790
  ▼q  (electronic bk.)

• 020

  ▼a  9781789953794
  ▼q  (electronic bk.)

• 035

  ▼a  2478486
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1155163086
  ▼z  (OCoLC)1155328035

• 040

  ▼a  YDX
  ▼b  eng
  ▼c  YDX
  ▼d  EBLCP
  ▼d  N$T
  ▼d  OCLCF
  ▼d  248023

• 050

  ▼a  QA76.73.P98

• 082

  ▼a  005.133
  ▼2  23

• 100

  ▼a  ANUBHAV SINGH;SAYAK PAUL.

• 245

  ▼a  HANDS-ON PYTHON DEEP LEARNING FOR THE WEB;INTEGRATING NEURAL NETWORK ARCHITECTURES TO BUILD SMART WEB APPS WITH FLASK, DJANGO, AND
  ▼h  [electronic resource].

• 260

  ▼a  [S.l.]:
  ▼b  PACKT PUBLISHING,
  ▼c  2020.

• 300

  ▼a  1 online resource.

• 505

  ▼a  Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Dedication -- Preface -- Table of Contents -- Section 1: Artificial Intelligence on the Web -- Chapter 1: Demystifying Artificial Intelligence and Fundamentals of Machine Learning -- Introduction to artificial intelligence and its types -- Factors responsible for AI propulsion -- Data -- Advancements in algorithms -- Advancements in hardware -- The democratization of high-performance computing -- ML -- the most popular form of AI -- What is DL? -- The relation between AI, ML, and DL

• 505

  ▼a  Revisiting the fundamentals of ML -- Types of ML -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Semi-supervised learning -- Necessary terminologies -- Train, test, and validation sets -- Bias and variance -- Overfitting and underfitting -- Training error and generalization error -- A standard ML workflow -- Data retrieval -- Data preparation -- Exploratory Data Analysis (EDA) -- Data processing and wrangling -- Feature engineering and extraction/selection -- Modeling -- Model training -- Model evaluation -- Model tuning -- Model comparison and selection

• 505

  ▼a  Deployment and monitoring -- The web before and after AI -- Chatbots -- Web analytics -- Spam filtering -- Search -- Biggest web-AI players and what are they doing with AI -- Google -- Google Search -- Google Translate -- Google Assistant -- Other products -- Facebook -- Fake profiles -- Fake news and disturbing content -- Other uses -- Amazon -- Alexa -- Amazon robotics -- DeepLens -- Summary -- Section 2: Using Deep Learning for Web Development -- Chapter 2: Getting Started with Deep Learning Using Python -- Demystifying neural networks -- Artificial neurons -- Anatomy of a linear neuron

• 505

  ▼a  Anatomy of a nonlinear neuron -- A note on the input and output layers of a neural network -- Gradient descent and backpropagation -- Different types of neural network -- Convolutional neural networks -- Recurrent neural networks -- Feeding the letters to the network -- Initializing the weight matrix and more -- Putting the weight matrices together -- Applying activation functions and the final output -- Exploring Jupyter Notebooks -- Installing Jupyter Notebook -- Installation using pip -- Installation using Anaconda -- Verifying the installation -- Jupyter Notebooks

• 505

  ▼a  Setting up a deep-learning-based cloud environment -- Setting up an AWS EC2 GPU deep learning environment -- Step 1: Creating an EC2 GPU-enabled instance -- Step 2: SSHing into your EC2 instance -- Step 3: Installing CUDA drivers on the GPU instance -- Step 4: Installing the Anaconda distribution of Python -- Step 5: Run Jupyter -- Deep learning on Crestle -- Other deep learning environments -- Exploring NumPy and pandas -- NumPy -- NumPy arrays -- Basic NumPy array operations -- NumPy arrays versus Python lists -- Array slicing over multiple rows and columns -- Assignment over slicing -- Pandas

• 520

  ▼a  This book will help you successfully implement deep learning in Python to create smart web applications from scratch. You will learn how deep learning can transform a simple web app into a smart, business-friendly product. You will also develop neural networks using open-source libraries and also integrate them with different web stack front-ends.

• 590

  ▼a  Master record variable field(s) change: 050, 082, 630, 650 - OCLC control number change

• 630

  ▼a  Django (Electronic resource)

• 630

  ▼a  Django (Electronic resource)
  ▼2  fast
  ▼0  (OCoLC)fst01780807

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Web applications.

• 650

  ▼a  Application software
  ▼x  Development.

• 650

  ▼a  Web site development.

• 650

  ▼a  Application software
  ▼x  Development.
  ▼2  fast
  ▼0  (OCoLC)fst00811707

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

• 650

  ▼a  Web applications.
  ▼2  fast
  ▼0  (OCoLC)fst01895855

• 650

  ▼a  Web site development.
  ▼2  fast
  ▼0  (OCoLC)fst01173243

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Singh, Anubhav
  ▼t  Hands-On Python Deep Learning for the Web : Integrating Neural Network Architectures to Build Smart Web Apps with Flask, Django, and TensorFlow.
  ▼d  Birmingham : Packt Publishing, Limited,c2020

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2478486

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  301282641

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6201980

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2478486

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T